Overview

Dataset statistics

Number of variables50
Number of observations101766
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory38.8 MiB
Average record size in memory400.0 B

Variable types

Numeric13
Categorical34
Boolean3

Alerts

examide has constant value "False" Constant
citoglipton has constant value "False" Constant
medical_specialty has a high cardinality: 73 distinct values High cardinality
diag_1 has a high cardinality: 717 distinct values High cardinality
diag_2 has a high cardinality: 749 distinct values High cardinality
diag_3 has a high cardinality: 790 distinct values High cardinality
encounter_id is highly correlated with patient_nbrHigh correlation
patient_nbr is highly correlated with encounter_idHigh correlation
encounter_id is highly correlated with patient_nbrHigh correlation
patient_nbr is highly correlated with encounter_idHigh correlation
glipizide-metformin is highly correlated with examide and 1 other fieldsHigh correlation
age is highly correlated with examide and 1 other fieldsHigh correlation
tolbutamide is highly correlated with examide and 1 other fieldsHigh correlation
metformin-rosiglitazone is highly correlated with examide and 1 other fieldsHigh correlation
insulin is highly correlated with examide and 3 other fieldsHigh correlation
race is highly correlated with examide and 1 other fieldsHigh correlation
examide is highly correlated with glipizide-metformin and 32 other fieldsHigh correlation
acarbose is highly correlated with examide and 1 other fieldsHigh correlation
weight is highly correlated with examide and 1 other fieldsHigh correlation
rosiglitazone is highly correlated with examide and 1 other fieldsHigh correlation
pioglitazone is highly correlated with examide and 1 other fieldsHigh correlation
readmitted is highly correlated with examide and 1 other fieldsHigh correlation
glipizide is highly correlated with examide and 1 other fieldsHigh correlation
nateglinide is highly correlated with examide and 1 other fieldsHigh correlation
acetohexamide is highly correlated with examide and 1 other fieldsHigh correlation
glimepiride-pioglitazone is highly correlated with examide and 1 other fieldsHigh correlation
glyburide is highly correlated with examide and 1 other fieldsHigh correlation
tolazamide is highly correlated with examide and 1 other fieldsHigh correlation
citoglipton is highly correlated with glipizide-metformin and 32 other fieldsHigh correlation
medical_specialty is highly correlated with examide and 1 other fieldsHigh correlation
troglitazone is highly correlated with examide and 1 other fieldsHigh correlation
miglitol is highly correlated with examide and 1 other fieldsHigh correlation
payer_code is highly correlated with examide and 1 other fieldsHigh correlation
metformin-pioglitazone is highly correlated with examide and 1 other fieldsHigh correlation
diabetesMed is highly correlated with insulin and 3 other fieldsHigh correlation
metformin is highly correlated with examide and 1 other fieldsHigh correlation
repaglinide is highly correlated with examide and 1 other fieldsHigh correlation
change is highly correlated with insulin and 3 other fieldsHigh correlation
chlorpropamide is highly correlated with examide and 1 other fieldsHigh correlation
A1Cresult is highly correlated with examide and 1 other fieldsHigh correlation
gender is highly correlated with examide and 1 other fieldsHigh correlation
glyburide-metformin is highly correlated with examide and 1 other fieldsHigh correlation
max_glu_serum is highly correlated with examide and 1 other fieldsHigh correlation
glimepiride is highly correlated with examide and 1 other fieldsHigh correlation
encounter_id is highly correlated with patient_nbr and 1 other fieldsHigh correlation
patient_nbr is highly correlated with encounter_idHigh correlation
age is highly correlated with medical_specialtyHigh correlation
admission_type_id is highly correlated with admission_source_id and 2 other fieldsHigh correlation
admission_source_id is highly correlated with admission_type_id and 1 other fieldsHigh correlation
payer_code is highly correlated with encounter_idHigh correlation
medical_specialty is highly correlated with age and 1 other fieldsHigh correlation
max_glu_serum is highly correlated with admission_type_id and 1 other fieldsHigh correlation
insulin is highly correlated with change and 1 other fieldsHigh correlation
change is highly correlated with insulin and 1 other fieldsHigh correlation
diabetesMed is highly correlated with insulin and 1 other fieldsHigh correlation
number_emergency is highly skewed (γ1 = 22.85558215) Skewed
encounter_id has unique values Unique
num_procedures has 46652 (45.8%) zeros Zeros
number_outpatient has 85027 (83.6%) zeros Zeros
number_emergency has 90383 (88.8%) zeros Zeros
number_inpatient has 67630 (66.5%) zeros Zeros

Reproduction

Analysis started2022-04-09 15:39:04.616772
Analysis finished2022-04-09 15:40:19.496057
Duration1 minute and 14.88 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

encounter_id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct101766
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean165201645.6
Minimum12522
Maximum443867222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2022-04-09T15:40:19.586079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum12522
5-th percentile27170784
Q184961194
median152388987
Q3230270887.5
95-th percentile378962843
Maximum443867222
Range443854700
Interquartile range (IQR)145309693.5

Descriptive statistics

Standard deviation102640296
Coefficient of variation (CV)0.6213031087
Kurtosis-0.1020713932
Mean165201645.6
Median Absolute Deviation (MAD)70921143
Skewness0.6991415513
Sum1.681191067 × 1013
Variance1.053503036 × 1016
MonotonicityNot monotonic
2022-04-09T15:40:19.720741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22783921
 
< 0.1%
1907920441
 
< 0.1%
1907900701
 
< 0.1%
1907897221
 
< 0.1%
1907868061
 
< 0.1%
1907850181
 
< 0.1%
1907814121
 
< 0.1%
1907758861
 
< 0.1%
1907645041
 
< 0.1%
1907603221
 
< 0.1%
Other values (101756)101756
> 99.9%
ValueCountFrequency (%)
125221
< 0.1%
157381
< 0.1%
166801
< 0.1%
282361
< 0.1%
357541
< 0.1%
369001
< 0.1%
409261
< 0.1%
425701
< 0.1%
558421
< 0.1%
622561
< 0.1%
ValueCountFrequency (%)
4438672221
< 0.1%
4438571661
< 0.1%
4438541481
< 0.1%
4438477821
< 0.1%
4438475481
< 0.1%
4438471761
< 0.1%
4438427781
< 0.1%
4438423401
< 0.1%
4438421361
< 0.1%
4438420701
< 0.1%

patient_nbr
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct71518
Distinct (%)70.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54330400.69
Minimum135
Maximum189502619
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2022-04-09T15:40:19.868331image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum135
5-th percentile1456971.75
Q123413221
median45505143
Q387545949.75
95-th percentile111480273
Maximum189502619
Range189502484
Interquartile range (IQR)64132728.75

Descriptive statistics

Standard deviation38696359.35
Coefficient of variation (CV)0.7122413759
Kurtosis-0.3473720444
Mean54330400.69
Median Absolute Deviation (MAD)32950134
Skewness0.4712807224
Sum5.528987557 × 1012
Variance1.497408227 × 1015
MonotonicityNot monotonic
2022-04-09T15:40:20.009694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8878589140
 
< 0.1%
4314090628
 
< 0.1%
166029323
 
< 0.1%
8822754023
 
< 0.1%
2319902123
 
< 0.1%
2364340522
 
< 0.1%
8442861322
 
< 0.1%
9270935121
 
< 0.1%
8878970720
 
< 0.1%
2990387720
 
< 0.1%
Other values (71508)101524
99.8%
ValueCountFrequency (%)
1352
 
< 0.1%
3781
 
< 0.1%
7291
 
< 0.1%
7741
 
< 0.1%
9271
 
< 0.1%
11525
< 0.1%
13051
 
< 0.1%
13143
< 0.1%
16291
 
< 0.1%
20251
 
< 0.1%
ValueCountFrequency (%)
1895026191
< 0.1%
1894814781
< 0.1%
1894451271
< 0.1%
1893658641
< 0.1%
1893510951
< 0.1%
1893494301
< 0.1%
1893320871
< 0.1%
1892988771
< 0.1%
1892578462
< 0.1%
1892157621
< 0.1%

race
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
Caucasian
76099 
AfricanAmerican
19210 
?
 
2273
Hispanic
 
2037
Other
 
1506

Length

Max length15
Median length9
Mean length9.849507694
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCaucasian
2nd rowCaucasian
3rd rowAfricanAmerican
4th rowCaucasian
5th rowCaucasian

Common Values

ValueCountFrequency (%)
Caucasian76099
74.8%
AfricanAmerican19210
 
18.9%
?2273
 
2.2%
Hispanic2037
 
2.0%
Other1506
 
1.5%
Asian641
 
0.6%

Length

2022-04-09T15:40:20.165219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-09T15:40:20.263544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
caucasian76099
74.8%
africanamerican19210
 
18.9%
2273
 
2.2%
hispanic2037
 
2.0%
other1506
 
1.5%
asian641
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

gender
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
Female
54708 
Male
47055 
Unknown/Invalid
 
3

Length

Max length15
Median length6
Mean length5.075496728
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Female54708
53.8%
Male47055
46.2%
Unknown/Invalid3
 
< 0.1%

Length

2022-04-09T15:40:20.399391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-09T15:40:20.478953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
female54708
53.8%
male47055
46.2%
unknown/invalid3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

age
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
[70-80)
26068 
[60-70)
22483 
[50-60)
17256 
[80-90)
17197 
[40-50)
9685 
Other values (5)
9077 

Length

Max length8
Median length7
Mean length7.025863255
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row[0-10)
2nd row[10-20)
3rd row[20-30)
4th row[30-40)
5th row[40-50)

Common Values

ValueCountFrequency (%)
[70-80)26068
25.6%
[60-70)22483
22.1%
[50-60)17256
17.0%
[80-90)17197
16.9%
[40-50)9685
 
9.5%
[30-40)3775
 
3.7%
[90-100)2793
 
2.7%
[20-30)1657
 
1.6%
[10-20)691
 
0.7%
[0-10)161
 
0.2%

Length

2022-04-09T15:40:20.625111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-09T15:40:20.749209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
70-8026068
25.6%
60-7022483
22.1%
50-6017256
17.0%
80-9017197
16.9%
40-509685
 
9.5%
30-403775
 
3.7%
90-1002793
 
2.7%
20-301657
 
1.6%
10-20691
 
0.7%
0-10161
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

weight
Categorical

HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
?
98569 
[75-100)
 
1336
[50-75)
 
897
[100-125)
 
625
[125-150)
 
145
Other values (5)
 
194

Length

Max length9
Median length1
Mean length1.217096083
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row?
2nd row?
3rd row?
4th row?
5th row?

Common Values

ValueCountFrequency (%)
?98569
96.9%
[75-100)1336
 
1.3%
[50-75)897
 
0.9%
[100-125)625
 
0.6%
[125-150)145
 
0.1%
[25-50)97
 
0.1%
[0-25)48
 
< 0.1%
[150-175)35
 
< 0.1%
[175-200)11
 
< 0.1%
>2003
 
< 0.1%

Length

2022-04-09T15:40:20.959104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-09T15:40:21.282025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
98569
96.9%
75-1001336
 
1.3%
50-75897
 
0.9%
100-125625
 
0.6%
125-150145
 
0.1%
25-5097
 
0.1%
0-2548
 
< 0.1%
150-17535
 
< 0.1%
175-20011
 
< 0.1%
2003
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

admission_type_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.024006053
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2022-04-09T15:40:21.455470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.44540283
Coefficient of variation (CV)0.7141296972
Kurtosis1.942476114
Mean2.024006053
Median Absolute Deviation (MAD)0
Skewness1.591984327
Sum205975
Variance2.08918934
MonotonicityNot monotonic
2022-04-09T15:40:21.546648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
153990
53.1%
318869
 
18.5%
218480
 
18.2%
65291
 
5.2%
54785
 
4.7%
8320
 
0.3%
721
 
< 0.1%
410
 
< 0.1%
ValueCountFrequency (%)
153990
53.1%
218480
 
18.2%
318869
 
18.5%
410
 
< 0.1%
54785
 
4.7%
65291
 
5.2%
721
 
< 0.1%
8320
 
0.3%
ValueCountFrequency (%)
8320
 
0.3%
721
 
< 0.1%
65291
 
5.2%
54785
 
4.7%
410
 
< 0.1%
318869
 
18.5%
218480
 
18.2%
153990
53.1%

discharge_disposition_id
Real number (ℝ≥0)

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.715641766
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2022-04-09T15:40:21.666287image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile18
Maximum28
Range27
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.280165509
Coefficient of variation (CV)1.421064204
Kurtosis6.003346764
Mean3.715641766
Median Absolute Deviation (MAD)0
Skewness2.563066993
Sum378126
Variance27.88014781
MonotonicityNot monotonic
2022-04-09T15:40:21.787906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
160234
59.2%
313954
 
13.7%
612902
 
12.7%
183691
 
3.6%
22128
 
2.1%
221993
 
2.0%
111642
 
1.6%
51184
 
1.2%
25989
 
1.0%
4815
 
0.8%
Other values (16)2234
 
2.2%
ValueCountFrequency (%)
160234
59.2%
22128
 
2.1%
313954
 
13.7%
4815
 
0.8%
51184
 
1.2%
612902
 
12.7%
7623
 
0.6%
8108
 
0.1%
921
 
< 0.1%
106
 
< 0.1%
ValueCountFrequency (%)
28139
 
0.1%
275
 
< 0.1%
25989
 
1.0%
2448
 
< 0.1%
23412
 
0.4%
221993
2.0%
202
 
< 0.1%
198
 
< 0.1%
183691
3.6%
1714
 
< 0.1%

admission_source_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.754436649
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2022-04-09T15:40:21.907360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median7
Q37
95-th percentile17
Maximum25
Range24
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.064080834
Coefficient of variation (CV)0.7062517293
Kurtosis1.744989372
Mean5.754436649
Median Absolute Deviation (MAD)0
Skewness1.029934878
Sum585606
Variance16.51675303
MonotonicityNot monotonic
2022-04-09T15:40:22.020967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
757494
56.5%
129565
29.1%
176781
 
6.7%
43187
 
3.1%
62264
 
2.2%
21104
 
1.1%
5855
 
0.8%
3187
 
0.2%
20161
 
0.2%
9125
 
0.1%
Other values (7)43
 
< 0.1%
ValueCountFrequency (%)
129565
29.1%
21104
 
1.1%
3187
 
0.2%
43187
 
3.1%
5855
 
0.8%
62264
 
2.2%
757494
56.5%
816
 
< 0.1%
9125
 
0.1%
108
 
< 0.1%
ValueCountFrequency (%)
252
 
< 0.1%
2212
 
< 0.1%
20161
 
0.2%
176781
6.7%
142
 
< 0.1%
131
 
< 0.1%
112
 
< 0.1%
108
 
< 0.1%
9125
 
0.1%
816
 
< 0.1%

time_in_hospital
Real number (ℝ≥0)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.395986872
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2022-04-09T15:40:22.132661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile11
Maximum14
Range13
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.985107767
Coefficient of variation (CV)0.6790529304
Kurtosis0.8502508405
Mean4.395986872
Median Absolute Deviation (MAD)2
Skewness1.133998719
Sum447362
Variance8.910868383
MonotonicityNot monotonic
2022-04-09T15:40:22.246389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
317756
17.4%
217224
16.9%
114208
14.0%
413924
13.7%
59966
9.8%
67539
7.4%
75859
 
5.8%
84391
 
4.3%
93002
 
2.9%
102342
 
2.3%
Other values (4)5555
 
5.5%
ValueCountFrequency (%)
114208
14.0%
217224
16.9%
317756
17.4%
413924
13.7%
59966
9.8%
67539
7.4%
75859
 
5.8%
84391
 
4.3%
93002
 
2.9%
102342
 
2.3%
ValueCountFrequency (%)
141042
 
1.0%
131210
 
1.2%
121448
 
1.4%
111855
 
1.8%
102342
 
2.3%
93002
 
2.9%
84391
4.3%
75859
5.8%
67539
7.4%
59966
9.8%

payer_code
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
?
40256 
MC
32439 
HM
6274 
SP
5007 
BC
4655 
Other values (13)
13135 

Length

Max length2
Median length2
Mean length1.60442584
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row?
2nd row?
3rd row?
4th row?
5th row?

Common Values

ValueCountFrequency (%)
?40256
39.6%
MC32439
31.9%
HM6274
 
6.2%
SP5007
 
4.9%
BC4655
 
4.6%
MD3532
 
3.5%
CP2533
 
2.5%
UN2448
 
2.4%
CM1937
 
1.9%
OG1033
 
1.0%
Other values (8)1652
 
1.6%

Length

2022-04-09T15:40:22.372228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
40256
39.6%
mc32439
31.9%
hm6274
 
6.2%
sp5007
 
4.9%
bc4655
 
4.6%
md3532
 
3.5%
cp2533
 
2.5%
un2448
 
2.4%
cm1937
 
1.9%
og1033
 
1.0%
Other values (8)1652
 
1.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

medical_specialty
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct73
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
?
49949 
InternalMedicine
14635 
Emergency/Trauma
7565 
Family/GeneralPractice
7440 
Cardiology
5352 
Other values (68)
16825 

Length

Max length36
Median length8
Mean length8.612670243
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowPediatrics-Endocrinology
2nd row?
3rd row?
4th row?
5th row?

Common Values

ValueCountFrequency (%)
?49949
49.1%
InternalMedicine14635
 
14.4%
Emergency/Trauma7565
 
7.4%
Family/GeneralPractice7440
 
7.3%
Cardiology5352
 
5.3%
Surgery-General3099
 
3.0%
Nephrology1613
 
1.6%
Orthopedics1400
 
1.4%
Orthopedics-Reconstructive1233
 
1.2%
Radiologist1140
 
1.1%
Other values (63)8340
 
8.2%

Length

2022-04-09T15:40:22.541984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
49949
49.1%
internalmedicine14635
 
14.4%
emergency/trauma7565
 
7.4%
family/generalpractice7440
 
7.3%
cardiology5352
 
5.3%
surgery-general3099
 
3.0%
nephrology1613
 
1.6%
orthopedics1400
 
1.4%
orthopedics-reconstructive1233
 
1.2%
radiologist1140
 
1.1%
Other values (63)8340
 
8.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

num_lab_procedures
Real number (ℝ≥0)

Distinct118
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.09564098
Minimum1
Maximum132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2022-04-09T15:40:22.714480image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q131
median44
Q357
95-th percentile73
Maximum132
Range131
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.67436225
Coefficient of variation (CV)0.4565278947
Kurtosis-0.2450735189
Mean43.09564098
Median Absolute Deviation (MAD)13
Skewness-0.2365439206
Sum4385671
Variance387.0805299
MonotonicityNot monotonic
2022-04-09T15:40:22.914957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13208
 
3.2%
432804
 
2.8%
442496
 
2.5%
452376
 
2.3%
382213
 
2.2%
402201
 
2.2%
462189
 
2.2%
412117
 
2.1%
422113
 
2.1%
472106
 
2.1%
Other values (108)77943
76.6%
ValueCountFrequency (%)
13208
3.2%
21101
 
1.1%
3668
 
0.7%
4378
 
0.4%
5286
 
0.3%
6282
 
0.3%
7323
 
0.3%
8366
 
0.4%
9933
 
0.9%
10838
 
0.8%
ValueCountFrequency (%)
1321
 
< 0.1%
1291
 
< 0.1%
1261
 
< 0.1%
1211
 
< 0.1%
1201
 
< 0.1%
1181
 
< 0.1%
1142
< 0.1%
1133
< 0.1%
1113
< 0.1%
1094
< 0.1%

num_procedures
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.339730362
Minimum0
Maximum6
Zeros46652
Zeros (%)45.8%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2022-04-09T15:40:23.067474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.705806979
Coefficient of variation (CV)1.273246489
Kurtosis0.8571103021
Mean1.339730362
Median Absolute Deviation (MAD)1
Skewness1.316414763
Sum136339
Variance2.90977745
MonotonicityNot monotonic
2022-04-09T15:40:23.169699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
046652
45.8%
120742
20.4%
212717
 
12.5%
39443
 
9.3%
64954
 
4.9%
44180
 
4.1%
53078
 
3.0%
ValueCountFrequency (%)
046652
45.8%
120742
20.4%
212717
 
12.5%
39443
 
9.3%
44180
 
4.1%
53078
 
3.0%
64954
 
4.9%
ValueCountFrequency (%)
64954
 
4.9%
53078
 
3.0%
44180
 
4.1%
39443
 
9.3%
212717
 
12.5%
120742
20.4%
046652
45.8%

num_medications
Real number (ℝ≥0)

Distinct75
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.02184423
Minimum1
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2022-04-09T15:40:23.308278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q110
median15
Q320
95-th percentile31
Maximum81
Range80
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.127566209
Coefficient of variation (CV)0.5072803163
Kurtosis3.468154915
Mean16.02184423
Median Absolute Deviation (MAD)5
Skewness1.326672134
Sum1630479
Variance66.05733248
MonotonicityNot monotonic
2022-04-09T15:40:23.456746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
136086
 
6.0%
126004
 
5.9%
115795
 
5.7%
155792
 
5.7%
145707
 
5.6%
165430
 
5.3%
105346
 
5.3%
174919
 
4.8%
94913
 
4.8%
184523
 
4.4%
Other values (65)47251
46.4%
ValueCountFrequency (%)
1262
 
0.3%
2470
 
0.5%
3900
 
0.9%
41417
 
1.4%
52017
 
2.0%
62699
2.7%
73484
3.4%
84353
4.3%
94913
4.8%
105346
5.3%
ValueCountFrequency (%)
811
 
< 0.1%
791
 
< 0.1%
752
 
< 0.1%
741
 
< 0.1%
723
< 0.1%
702
 
< 0.1%
695
< 0.1%
687
< 0.1%
677
< 0.1%
665
< 0.1%

number_outpatient
Real number (ℝ≥0)

ZEROS

Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3693571527
Minimum0
Maximum42
Zeros85027
Zeros (%)83.6%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2022-04-09T15:40:23.612973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum42
Range42
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.267265097
Coefficient of variation (CV)3.431001911
Kurtosis147.9077363
Mean0.3693571527
Median Absolute Deviation (MAD)0
Skewness8.832958927
Sum37588
Variance1.605960825
MonotonicityNot monotonic
2022-04-09T15:40:23.731313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
085027
83.6%
18547
 
8.4%
23594
 
3.5%
32042
 
2.0%
41099
 
1.1%
5533
 
0.5%
6303
 
0.3%
7155
 
0.2%
898
 
0.1%
983
 
0.1%
Other values (29)285
 
0.3%
ValueCountFrequency (%)
085027
83.6%
18547
 
8.4%
23594
 
3.5%
32042
 
2.0%
41099
 
1.1%
5533
 
0.5%
6303
 
0.3%
7155
 
0.2%
898
 
0.1%
983
 
0.1%
ValueCountFrequency (%)
421
< 0.1%
401
< 0.1%
391
< 0.1%
381
< 0.1%
371
< 0.1%
362
< 0.1%
352
< 0.1%
341
< 0.1%
332
< 0.1%
292
< 0.1%

number_emergency
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1978362125
Minimum0
Maximum76
Zeros90383
Zeros (%)88.8%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2022-04-09T15:40:23.855689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum76
Range76
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9304722684
Coefficient of variation (CV)4.703245461
Kurtosis1191.686726
Mean0.1978362125
Median Absolute Deviation (MAD)0
Skewness22.85558215
Sum20133
Variance0.8657786423
MonotonicityNot monotonic
2022-04-09T15:40:23.969231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
090383
88.8%
17677
 
7.5%
22042
 
2.0%
3725
 
0.7%
4374
 
0.4%
5192
 
0.2%
694
 
0.1%
773
 
0.1%
850
 
< 0.1%
1034
 
< 0.1%
Other values (23)122
 
0.1%
ValueCountFrequency (%)
090383
88.8%
17677
 
7.5%
22042
 
2.0%
3725
 
0.7%
4374
 
0.4%
5192
 
0.2%
694
 
0.1%
773
 
0.1%
850
 
< 0.1%
933
 
< 0.1%
ValueCountFrequency (%)
761
< 0.1%
641
< 0.1%
631
< 0.1%
541
< 0.1%
461
< 0.1%
421
< 0.1%
371
< 0.1%
291
< 0.1%
281
< 0.1%
252
< 0.1%

number_inpatient
Real number (ℝ≥0)

ZEROS

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6355659061
Minimum0
Maximum21
Zeros67630
Zeros (%)66.5%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2022-04-09T15:40:24.098565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum21
Range21
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.26286329
Coefficient of variation (CV)1.986990299
Kurtosis20.71939695
Mean0.6355659061
Median Absolute Deviation (MAD)0
Skewness3.614138992
Sum64679
Variance1.594823689
MonotonicityNot monotonic
2022-04-09T15:40:24.207181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
067630
66.5%
119521
 
19.2%
27566
 
7.4%
33411
 
3.4%
41622
 
1.6%
5812
 
0.8%
6480
 
0.5%
7268
 
0.3%
8151
 
0.1%
9111
 
0.1%
Other values (11)194
 
0.2%
ValueCountFrequency (%)
067630
66.5%
119521
 
19.2%
27566
 
7.4%
33411
 
3.4%
41622
 
1.6%
5812
 
0.8%
6480
 
0.5%
7268
 
0.3%
8151
 
0.1%
9111
 
0.1%
ValueCountFrequency (%)
211
 
< 0.1%
192
 
< 0.1%
181
 
< 0.1%
171
 
< 0.1%
166
 
< 0.1%
159
 
< 0.1%
1410
 
< 0.1%
1320
< 0.1%
1234
< 0.1%
1149
< 0.1%

diag_1
Categorical

HIGH CARDINALITY

Distinct717
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
428
 
6862
414
 
6581
786
 
4016
410
 
3614
486
 
3508
Other values (712)
77185 

Length

Max length6
Median length3
Mean length3.175215691
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique82 ?
Unique (%)0.1%

Sample

1st row250.83
2nd row276
3rd row648
4th row8
5th row197

Common Values

ValueCountFrequency (%)
4286862
 
6.7%
4146581
 
6.5%
7864016
 
3.9%
4103614
 
3.6%
4863508
 
3.4%
4272766
 
2.7%
4912275
 
2.2%
7152151
 
2.1%
6822042
 
2.0%
4342028
 
2.0%
Other values (707)65923
64.8%

Length

2022-04-09T15:40:24.327460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4286862
 
6.7%
4146581
 
6.5%
7864016
 
3.9%
4103614
 
3.6%
4863508
 
3.4%
4272766
 
2.7%
4912275
 
2.2%
7152151
 
2.1%
6822042
 
2.0%
4342028
 
2.0%
Other values (707)65923
64.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

diag_2
Categorical

HIGH CARDINALITY

Distinct749
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
276
 
6752
428
 
6662
250
 
6071
427
 
5036
401
 
3736
Other values (744)
73509 

Length

Max length6
Median length3
Mean length3.166194996
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique124 ?
Unique (%)0.1%

Sample

1st row?
2nd row250.01
3rd row250
4th row250.43
5th row157

Common Values

ValueCountFrequency (%)
2766752
 
6.6%
4286662
 
6.5%
2506071
 
6.0%
4275036
 
4.9%
4013736
 
3.7%
4963305
 
3.2%
5993288
 
3.2%
4032823
 
2.8%
4142650
 
2.6%
4112566
 
2.5%
Other values (739)58877
57.9%

Length

2022-04-09T15:40:24.633226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2766752
 
6.6%
4286662
 
6.5%
2506071
 
6.0%
4275036
 
4.9%
4013736
 
3.7%
4963305
 
3.2%
5993288
 
3.2%
4032823
 
2.8%
4142650
 
2.6%
4112566
 
2.5%
Other values (739)58877
57.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

diag_3
Categorical

HIGH CARDINALITY

Distinct790
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
250
11555 
401
8289 
276
 
5175
428
 
4577
427
 
3955
Other values (785)
68215 

Length

Max length6
Median length3
Mean length3.111658118
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique122 ?
Unique (%)0.1%

Sample

1st row?
2nd row255
3rd rowV27
4th row403
5th row250

Common Values

ValueCountFrequency (%)
25011555
 
11.4%
4018289
 
8.1%
2765175
 
5.1%
4284577
 
4.5%
4273955
 
3.9%
4143664
 
3.6%
4962605
 
2.6%
4032357
 
2.3%
5851992
 
2.0%
2721969
 
1.9%
Other values (780)55628
54.7%

Length

2022-04-09T15:40:24.743015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
25011555
 
11.4%
4018289
 
8.1%
2765175
 
5.1%
4284577
 
4.5%
4273955
 
3.9%
4143664
 
3.6%
4962605
 
2.6%
4032357
 
2.3%
5851992
 
2.0%
2721969
 
1.9%
Other values (780)55628
54.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

number_diagnoses
Real number (ℝ≥0)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.422606765
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2022-04-09T15:40:24.842818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q16
median8
Q39
95-th percentile9
Maximum16
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.933600145
Coefficient of variation (CV)0.2605014931
Kurtosis-0.07905602427
Mean7.422606765
Median Absolute Deviation (MAD)1
Skewness-0.8767462388
Sum755369
Variance3.738809521
MonotonicityNot monotonic
2022-04-09T15:40:24.937681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
949474
48.6%
511393
 
11.2%
810616
 
10.4%
710393
 
10.2%
610161
 
10.0%
45537
 
5.4%
32835
 
2.8%
21023
 
1.0%
1219
 
0.2%
1645
 
< 0.1%
Other values (6)70
 
0.1%
ValueCountFrequency (%)
1219
 
0.2%
21023
 
1.0%
32835
 
2.8%
45537
 
5.4%
511393
 
11.2%
610161
 
10.0%
710393
 
10.2%
810616
 
10.4%
949474
48.6%
1017
 
< 0.1%
ValueCountFrequency (%)
1645
 
< 0.1%
1510
 
< 0.1%
147
 
< 0.1%
1316
 
< 0.1%
129
 
< 0.1%
1111
 
< 0.1%
1017
 
< 0.1%
949474
48.6%
810616
 
10.4%
710393
 
10.2%

max_glu_serum
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
None
96420 
Norm
 
2597
>200
 
1485
>300
 
1264

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None96420
94.7%
Norm2597
 
2.6%
>2001485
 
1.5%
>3001264
 
1.2%

Length

2022-04-09T15:40:25.056314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-09T15:40:25.142511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
none96420
94.7%
norm2597
 
2.6%
2001485
 
1.5%
3001264
 
1.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

A1Cresult
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
None
84748 
>8
 
8216
Norm
 
4990
>7
 
3812

Length

Max length4
Median length4
Mean length3.763614567
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None84748
83.3%
>88216
 
8.1%
Norm4990
 
4.9%
>73812
 
3.7%

Length

2022-04-09T15:40:25.250910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-09T15:40:25.345739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
none84748
83.3%
88216
 
8.1%
norm4990
 
4.9%
73812
 
3.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

metformin
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
81778 
Steady
18346 
Up
 
1067
Down
 
575

Length

Max length6
Median length2
Mean length2.732405715
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No81778
80.4%
Steady18346
 
18.0%
Up1067
 
1.0%
Down575
 
0.6%

Length

2022-04-09T15:40:25.472141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-09T15:40:25.584683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no81778
80.4%
steady18346
 
18.0%
up1067
 
1.0%
down575
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

repaglinide
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
100227 
Steady
 
1384
Up
 
110
Down
 
45

Length

Max length6
Median length2
Mean length2.05528369
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No100227
98.5%
Steady1384
 
1.4%
Up110
 
0.1%
Down45
 
< 0.1%

Length

2022-04-09T15:40:25.774736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-09T15:40:25.916122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no100227
98.5%
steady1384
 
1.4%
up110
 
0.1%
down45
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

nateglinide
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101063 
Steady
 
668
Up
 
24
Down
 
11

Length

Max length6
Median length2
Mean length2.026472496
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No101063
99.3%
Steady668
 
0.7%
Up24
 
< 0.1%
Down11
 
< 0.1%

Length

2022-04-09T15:40:26.042240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-09T15:40:26.146805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no101063
99.3%
steady668
 
0.7%
up24
 
< 0.1%
down11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

chlorpropamide
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101680 
Steady
 
79
Up
 
6
Down
 
1

Length

Max length6
Median length2
Mean length2.003124816
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No101680
99.9%
Steady79
 
0.1%
Up6
 
< 0.1%
Down1
 
< 0.1%

Length

2022-04-09T15:40:26.260830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-09T15:40:26.356725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no101680
99.9%
steady79
 
0.1%
up6
 
< 0.1%
down1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

glimepiride
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
96575 
Steady
 
4670
Up
 
327
Down
 
194

Length

Max length6
Median length2
Mean length2.187371028
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No96575
94.9%
Steady4670
 
4.6%
Up327
 
0.3%
Down194
 
0.2%

Length

2022-04-09T15:40:26.465952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-09T15:40:26.564344image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no96575
94.9%
steady4670
 
4.6%
up327
 
0.3%
down194
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

acetohexamide
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101765 
Steady
 
1

Length

Max length6
Median length2
Mean length2.000039306
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No101765
> 99.9%
Steady1
 
< 0.1%

Length

2022-04-09T15:40:26.677127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-09T15:40:26.769543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no101765
> 99.9%
steady1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

glipizide
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
89080 
Steady
11356 
Up
 
770
Down
 
560

Length

Max length6
Median length2
Mean length2.45736297
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowSteady
4th rowNo
5th rowSteady

Common Values

ValueCountFrequency (%)
No89080
87.5%
Steady11356
 
11.2%
Up770
 
0.8%
Down560
 
0.6%

Length

2022-04-09T15:40:26.854487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-09T15:40:26.943014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no89080
87.5%
steady11356
 
11.2%
up770
 
0.8%
down560
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

glyburide
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
91116 
Steady
9274 
Up
 
812
Down
 
564

Length

Max length6
Median length2
Mean length2.375606784
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No91116
89.5%
Steady9274
 
9.1%
Up812
 
0.8%
Down564
 
0.6%

Length

2022-04-09T15:40:27.050821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-09T15:40:27.150123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no91116
89.5%
steady9274
 
9.1%
up812
 
0.8%
down564
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

tolbutamide
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101743 
Steady
 
23

Length

Max length6
Median length2
Mean length2.000904035
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No101743
> 99.9%
Steady23
 
< 0.1%

Length

2022-04-09T15:40:27.258594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-09T15:40:27.348224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no101743
> 99.9%
steady23
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

pioglitazone
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
94438 
Steady
 
6976
Up
 
234
Down
 
118

Length

Max length6
Median length2
Mean length2.276516715
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No94438
92.8%
Steady6976
 
6.9%
Up234
 
0.2%
Down118
 
0.1%

Length

2022-04-09T15:40:27.433501image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-09T15:40:27.524854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no94438
92.8%
steady6976
 
6.9%
up234
 
0.2%
down118
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

rosiglitazone
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
95401 
Steady
 
6100
Up
 
178
Down
 
87

Length

Max length6
Median length2
Mean length2.241475542
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No95401
93.7%
Steady6100
 
6.0%
Up178
 
0.2%
Down87
 
0.1%

Length

2022-04-09T15:40:27.637492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-09T15:40:27.764709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no95401
93.7%
steady6100
 
6.0%
up178
 
0.2%
down87
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

acarbose
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101458 
Steady
 
295
Up
 
10
Down
 
3

Length

Max length6
Median length2
Mean length2.011654187
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No101458
99.7%
Steady295
 
0.3%
Up10
 
< 0.1%
Down3
 
< 0.1%

Length

2022-04-09T15:40:27.896882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-09T15:40:28.213881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no101458
99.7%
steady295
 
0.3%
up10
 
< 0.1%
down3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

miglitol
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101728 
Steady
 
31
Down
 
5
Up
 
2

Length

Max length6
Median length2
Mean length2.001316746
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No101728
> 99.9%
Steady31
 
< 0.1%
Down5
 
< 0.1%
Up2
 
< 0.1%

Length

2022-04-09T15:40:28.323694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-09T15:40:28.439883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no101728
> 99.9%
steady31
 
< 0.1%
down5
 
< 0.1%
up2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

troglitazone
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101763 
Steady
 
3

Length

Max length6
Median length2
Mean length2.000117918
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No101763
> 99.9%
Steady3
 
< 0.1%

Length

2022-04-09T15:40:28.549465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-09T15:40:28.644940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no101763
> 99.9%
steady3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

tolazamide
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101727 
Steady
 
38
Up
 
1

Length

Max length6
Median length2
Mean length2.001493623
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No101727
> 99.9%
Steady38
 
< 0.1%
Up1
 
< 0.1%

Length

2022-04-09T15:40:28.762512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-09T15:40:28.855168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no101727
> 99.9%
steady38
 
< 0.1%
up1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

examide
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.5 KiB
False
101766 
ValueCountFrequency (%)
False101766
100.0%
2022-04-09T15:40:28.910297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

citoglipton
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.5 KiB
False
101766 
ValueCountFrequency (%)
False101766
100.0%
2022-04-09T15:40:28.944020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

insulin
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
47383 
Steady
30849 
Down
12218 
Up
11316 

Length

Max length6
Median length2
Mean length3.45266592
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowUp
3rd rowNo
4th rowUp
5th rowSteady

Common Values

ValueCountFrequency (%)
No47383
46.6%
Steady30849
30.3%
Down12218
 
12.0%
Up11316
 
11.1%

Length

2022-04-09T15:40:29.019743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-09T15:40:29.115948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no47383
46.6%
steady30849
30.3%
down12218
 
12.0%
up11316
 
11.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

glyburide-metformin
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101060 
Steady
 
692
Up
 
8
Down
 
6

Length

Max length6
Median length2
Mean length2.027317572
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No101060
99.3%
Steady692
 
0.7%
Up8
 
< 0.1%
Down6
 
< 0.1%

Length

2022-04-09T15:40:29.228142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-09T15:40:29.326887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no101060
99.3%
steady692
 
0.7%
up8
 
< 0.1%
down6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

glipizide-metformin
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101753 
Steady
 
13

Length

Max length6
Median length2
Mean length2.000510976
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No101753
> 99.9%
Steady13
 
< 0.1%

Length

2022-04-09T15:40:29.436382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-09T15:40:29.531862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no101753
> 99.9%
steady13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

glimepiride-pioglitazone
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101765 
Steady
 
1

Length

Max length6
Median length2
Mean length2.000039306
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No101765
> 99.9%
Steady1
 
< 0.1%

Length

2022-04-09T15:40:29.621452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-09T15:40:29.710686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no101765
> 99.9%
steady1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

metformin-rosiglitazone
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101764 
Steady
 
2

Length

Max length6
Median length2
Mean length2.000078612
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No101764
> 99.9%
Steady2
 
< 0.1%

Length

2022-04-09T15:40:29.796185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-09T15:40:29.884156image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no101764
> 99.9%
steady2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

metformin-pioglitazone
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101765 
Steady
 
1

Length

Max length6
Median length2
Mean length2.000039306
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No101765
> 99.9%
Steady1
 
< 0.1%

Length

2022-04-09T15:40:29.970735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-09T15:40:30.065607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no101765
> 99.9%
steady1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

change
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
54755 
Ch
47011 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowCh
3rd rowNo
4th rowCh
5th rowCh

Common Values

ValueCountFrequency (%)
No54755
53.8%
Ch47011
46.2%

Length

2022-04-09T15:40:30.152205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-09T15:40:30.241490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no54755
53.8%
ch47011
46.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

diabetesMed
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.5 KiB
True
78363 
False
23403 
ValueCountFrequency (%)
True78363
77.0%
False23403
 
23.0%
2022-04-09T15:40:30.293178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

readmitted
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
NO
54864 
>30
35545 
<30
11357 

Length

Max length3
Median length2
Mean length2.460880844
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNO
2nd row>30
3rd rowNO
4th rowNO
5th rowNO

Common Values

ValueCountFrequency (%)
NO54864
53.9%
>3035545
34.9%
<3011357
 
11.2%

Length

2022-04-09T15:40:30.390295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-09T15:40:30.477103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no54864
53.9%
3046902
46.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-04-09T15:40:08.026234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:41.418619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:43.799246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:45.964444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:48.252175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:50.416685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:52.667196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:55.022873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:57.164310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:59.458611image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:01.671358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:03.854630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:05.993306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:08.212722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:41.632672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:43.959232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:46.109437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:48.391983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:50.630642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:52.882523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:55.178633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:57.304846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:59.668441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:01.811677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:03.997008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:06.133591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:08.426586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:41.788308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:44.140500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:46.280191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:48.580642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:50.830572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:53.099361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:55.353698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:57.461797image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:59.845904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:01.988331image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:04.158343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:06.296141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:08.588313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:41.936639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:44.315107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:46.430373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:48.735590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:51.008749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:53.465580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:55.512948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:57.635950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:00.004088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:02.152368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:04.352829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:06.434832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:08.737622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:42.086322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:44.486664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:46.575728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:48.957583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:51.199922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:53.625028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:55.689292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:57.790954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:00.181359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:02.301094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:04.540758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:06.575970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:09.112220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:42.283617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:44.641389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:46.718239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:49.101862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:51.356068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:53.781905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:55.846300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:57.996269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:00.363823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:02.447342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:04.688880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:06.720220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:09.282812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:42.435116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:44.803457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:46.863055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:49.247430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:51.527490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:53.943057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:55.992514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:58.165077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:00.517116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:02.634271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:04.836961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:06.871454image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:09.440307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:42.827029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:44.958616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:47.015256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:49.401814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:51.692736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:54.105736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:56.150307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:58.351840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:00.682810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:02.786463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:04.991019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:07.035653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:09.593548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:42.981232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:45.116769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:47.160981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:49.571558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:51.863976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:54.288046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:56.310834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:58.702498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:00.847461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:02.947551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:05.139930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:07.189574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:09.751769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:43.149541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:45.279784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:47.318372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:49.734224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:52.025830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:54.444652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:56.488920image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:58.862856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:01.012579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:03.091438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:05.305781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:07.357429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:09.896573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:43.301218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:45.430196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:47.468716image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:49.887223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:52.183646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:54.585817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:56.680363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:59.000232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:01.165628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:03.222277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:05.446749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:07.508194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:10.063770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:43.473136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:45.618522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:47.836559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:50.090639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:52.368424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:54.735871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:56.844624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:59.151392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:01.333634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:03.367397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:05.600724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:07.685525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:10.238716image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:43.654369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:45.789811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:48.035630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:50.244719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:52.510028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:54.873813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:57.008054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:39:59.301676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:01.492560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:03.500882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:05.793574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-09T15:40:07.829223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-04-09T15:40:30.565133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-09T15:40:30.870909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-09T15:40:31.188264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-09T15:40:31.721371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-04-09T15:40:32.263137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-09T15:40:10.993400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-09T15:40:17.188259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

encounter_idpatient_nbrracegenderageweightadmission_type_iddischarge_disposition_idadmission_source_idtime_in_hospitalpayer_codemedical_specialtynum_lab_proceduresnum_proceduresnum_medicationsnumber_outpatientnumber_emergencynumber_inpatientdiag_1diag_2diag_3number_diagnosesmax_glu_serumA1Cresultmetforminrepaglinidenateglinidechlorpropamideglimepirideacetohexamideglipizideglyburidetolbutamidepioglitazonerosiglitazoneacarbosemiglitoltroglitazonetolazamideexamidecitogliptoninsulinglyburide-metforminglipizide-metforminglimepiride-pioglitazonemetformin-rosiglitazonemetformin-pioglitazonechangediabetesMedreadmitted
022783928222157CaucasianFemale[0-10)?62511?Pediatrics-Endocrinology4101000250.83??1NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNO
114919055629189CaucasianFemale[10-20)?1173??59018000276250.012559NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoUpNoNoNoNoNoChYes>30
26441086047875AfricanAmericanFemale[20-30)?1172??11513201648250V276NoneNoneNoNoNoNoNoNoSteadyNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoYesNO
350036482442376CaucasianMale[30-40)?1172??441160008250.434037NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoUpNoNoNoNoNoChYesNO
41668042519267CaucasianMale[40-50)?1171??51080001971572505NoneNoneNoNoNoNoNoNoSteadyNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoChYesNO
53575482637451CaucasianMale[50-60)?2123??316160004144112509NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoNoYes>30
65584284259809CaucasianMale[60-70)?3124??70121000414411V457NoneNoneSteadyNoNoNoSteadyNoNoNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoChYesNO
763768114882984CaucasianMale[70-80)?1175??730120004284922508NoneNoneNoNoNoNoNoNoNoSteadyNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoYes>30
81252248330783CaucasianFemale[80-90)?21413??68228000398427388NoneNoneNoNoNoNoNoNoSteadyNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoChYesNO
91573863555939CaucasianFemale[90-100)?33412?InternalMedicine333180004341984868NoneNoneNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoNoSteadyNoNoNoNoNoChYesNO

Last rows

encounter_idpatient_nbrracegenderageweightadmission_type_iddischarge_disposition_idadmission_source_idtime_in_hospitalpayer_codemedical_specialtynum_lab_proceduresnum_proceduresnum_medicationsnumber_outpatientnumber_emergencynumber_inpatientdiag_1diag_2diag_3number_diagnosesmax_glu_serumA1Cresultmetforminrepaglinidenateglinidechlorpropamideglimepirideacetohexamideglipizideglyburidetolbutamidepioglitazonerosiglitazoneacarbosemiglitoltroglitazonetolazamideexamidecitogliptoninsulinglyburide-metforminglipizide-metforminglimepiride-pioglitazonemetformin-rosiglitazonemetformin-pioglitazonechangediabetesMedreadmitted
101756443842070140199494OtherFemale[60-70)?1172MD?466171119965854039NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoNoYes>30
101757443842136181593374CaucasianFemale[70-80)?1175??211160014915185119NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoNoYesNO
101758443842340120975314CaucasianFemale[80-90)?1175MC?7612201029283049NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoUpNoNoNoNoNoChYesNO
10175944384277886472243CaucasianMale[80-90)?1171MC?10153004357842507NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoUpNoNoNoNoNoChYesNO
10176044384717650375628AfricanAmericanFemale[60-70)?1176DM?451253123454384129NoneNoneNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoNoDownNoNoNoNoNoChYes>30
101761443847548100162476AfricanAmericanMale[70-80)?1373MC?51016000250.132914589None>8SteadyNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoDownNoNoNoNoNoChYes>30
10176244384778274694222AfricanAmericanFemale[80-90)?1455MC?333180015602767879NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoNoYesNO
10176344385414841088789CaucasianMale[70-80)?1171MC?53091003859029613NoneNoneSteadyNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoDownNoNoNoNoNoChYesNO
10176444385716631693671CaucasianFemale[80-90)?23710MCSurgery-General452210019962859989NoneNoneNoNoNoNoNoNoSteadyNoNoSteadyNoNoNoNoNoNoNoUpNoNoNoNoNoChYesNO
101765443867222175429310CaucasianMale[70-80)?1176??13330005305307879NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNO